Deep generative models have been widely used for their ability to generate realistic data samples in various areas, such as images, molecules, text, and speech. One major goal of data generation is controllability, namely to generate new data with desired properties. Despite growing interest in the area of controllable generation, significant challenges still remain, including 1) disentangling desired properties with unrelated latent variables, 2) out-of-distribution property control, and 3) objective optimization for out-of-distribution property control. To address these challenges, in this paper, we propose a general framework to enhance VAE-based data generators with property controllability and ensure disentanglement. Our proposed objective can be optimized on both data seen and unseen in the training set. We propose a training procedure to train the objective in a semi-supervised manner by iteratively conducting mutual mappings between the data and properties. The proposed framework is implemented on four VAE-based controllable generators to evaluate its performance on property error, disentanglement, generation quality, and training time. The results indicate that our proposed framework enables more precise control over the properties of generated samples in a short training time, ensuring the disentanglement and keeping the validity of the generated samples.

翻译：深度生成模型因其在图像、分子、文本和语音等多个领域生成逼真数据样本的能力而得到广泛应用。数据生成的一个主要目标是可控性，即生成具有所需属性的新数据。尽管可控生成领域引起了越来越多的关注，但仍面临重大挑战，包括：1）将所需属性与无关的潜在变量解耦，2）分布外属性控制，以及3）分布外属性控制的目标优化。为解决这些挑战，本文提出一个通用框架，以增强基于VAE的数据生成器的属性可控性并确保解耦性。我们提出的优化目标可同时用于训练集中已见和未见的数据。我们提出一种训练过程，通过迭代执行数据与属性之间的相互映射，以半监督方式训练该目标。该框架在四种基于VAE的可控生成器上实现，以评估其在属性误差、解耦性、生成质量和训练时间方面的性能。结果表明，我们的框架能在较短的训练时间内实现对生成样本属性的更精确控制，同时确保解耦性并保持生成样本的有效性。